With the advent of deep learning and increasing use of brain MRIs, a great amount of
interest has arisen in automated anomaly segmentation to improve clinical workflows; …

Pathological brain appearances may be so heterogeneous as to be intelligible only as
anomalies, defined by their deviation from normality rather than any specific pathological …

Abstract Purpose Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis
of neurological diseases. Recently, deep learning methods for unsupervised anomaly …

Brain pathologies can vary greatly in size and shape, ranging from few pixels (ie MS lesions)
to large, space-occupying tumors. Recently proposed Autoencoder-based methods for …

In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in
Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying …

Reliably modeling normality and differentiating abnormal appearances from normal cases is
a very appealing approach for detecting pathologies in medical images. A plethora of such …

Purpose To develop an unsupervised deep learning model on MR images of normal brain
anatomy to automatically detect deviations indicative of pathologic states on abnormal MR …

Pathological brain appearances may be so heterogeneous as to be intelligible only as
anomalies, defined by their deviation from normality rather than any specific set of …

In recent years, data-driven machine learning (ML) methods have revolutionized the
computer vision community by providing novel efficient solutions to many unsolved …

The main benefit of unsupervised anomaly detection is the ability to identify arbitrary
instances of pathologies even in the absence of training labels or sufficient examples of the …